Xiaolu Liu, Song Wang, Wentong Li, Ruizi Yang, Junbo Chen, Jianke Zhu
[Paper] (arXiv). CVPR2024
- [2024/4/13]: We release the code and checkpoint for camera modality.
We propose MGMap, a mask-guided approach that effectively highlights the informative regions and achieves precise map element localization by introducing the learned masks. Specifically, MGMap employs learned masks based on the enhanced multi-scale BEV features from two perspectives. At the instance level, we propose the Mask-activated instance (MAI) decoder, which incorporates global instance and structural information into instance queries by the activation of instance masks. At the point level, a novel position-guided mask patch refinement (PG-MPR) module is designed to refine point locations from a finer-grained perspective, enabling the extraction of point-specific patch information. Compared to the baselines, our proposed MGMap achieves a notable promotion of around 10 mAP for different input modalities. Extensive experiments also demonstrate that our approach showcases strong robustness and generalization capabilities.
-
Release the code.
-
Add configs for LiDAR and fusion modalities.
-
Release pre-trained models.
| Model | Modality | Backbone | Epoch | mAP | FPS | Config | Download |
|---|---|---|---|---|---|---|---|
| MGMap | Camera | R50 | 30 | 61.4 | 11.6 | config | model |
| MGMap | Lidar | Second | 24 | 67.9 | 5.5 | config | model |
| MGMap | Camera&Lidar | R50&Sec | 24 | 71.7 | 4.8 | config | model |
MGMap is based on mmdetection3d. It is also greatly inspired by the following outstanding contributions to the open-source community: BEVFormer, HDMapNet, MapTR, SparseInst.
If the paper and code help your research, please kindly cite:
@misc{liu2024mgmap,
title={MGMap: Mask-Guided Learning for Online Vectorized HD Map Construction},
author={Xiaolu Liu and Song Wang and Wentong Li and Ruizi Yang and Junbo Chen and Jianke Zhu},
year={2024},
eprint={2404.00876},
archivePrefix={arXiv},
primaryClass={cs.CV}
}